Predicting Electric Vehicle Adoption in the EU: Analyzing Classification Performance and Influencing Attributes Across Countries, Gender, and Education Level

Electric vehicles (EVs) have been one of the trending technologies in recent decades, as they are expected to transform the current automotive technology and transportation systems. To this end, the scope of this study is analyzing survey data on European consumers' EV purchase decisions. The objective is comparing the predictive quality of various classification algorithms in predicting EV adoption, across country, gender and education level of the participants, as well as the analysis of the influencing attributes. Initially, the data is filtered for each value of the chosen categorical attribute (country, gender or education level) with the missing values being imputed. Then, several classification algorithms in the Python sklearn package are applied through 5-fold-cross validation and the performance of the algorithms are compared based on standard classification metrics. There are notable variations in classification performance and influencing attributes depending on the values of the selected categorical attributes.